Neuro-fuzzy pattern classification for fault diagnosis in nuclear components

Zio, Enrico; Gola, Giulio

doi:10.1016/j.anucene.2005.12.008

Cited by 37 publications

(22 citation statements)

References 9 publications

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“…The overall plant has a global alarming and fault diagnosis system, which links all individual subsystems. As the control system of the plant can be tested by NFDS to define the fault if found, also all parts of the plant can be tested by a pattern recognition NFDS techniques [8].…”

Section: A Neuro Fuzzy Approachmentioning

confidence: 99%

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Safety Improvement of Nuclear Power Reactor Using Soft Computing Techniques

Muzzammil¹

2013

IOSR-JECE

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Nowadays, Nuclear Reactors (NR) are large in scale and complex, they are expected to be operated with high levels of reliability and safety. Hence to increase plant safety, to achieve, maintain system stability and assure satisfactory in order to meet the increasing demands for automated system and to detect and diagnose system failures and malfunctions. When a plant malfunction occurs, a great data influx is occurred. This paper proposes a support system based on neuro fuzzy approach conjunction with Genetic Algorithm that assist alarming and diagnosis system. Throughout this framework Neurofuzzy fault diagnosis system is employed to diagnosis the fault of nuclear reactors. Hence to overcome weak points of both neuro learning and linguistic based approaches by which the integrated system will inherit the strength of both approaches and to optimize the Neurofuzzy outcomes using Genetic Algorithm resulting to show the efficiency is obtained by GA is greater and the inaccurate information of the alarming system also compared with Neurofuzzy diagnosis system.

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Section: A Neuro Fuzzy Approachmentioning

confidence: 99%

“…It is the combinations of artificial neural networks and fuzzy logic. Neurofuzzy hybridization is widely termed as Neurofuzzy system (NFS) [8]. And by using Genetic Algorithm the diagnosis outputs were optimized and this will give a clear view for operators on making the decision.…”

Section: Introductionmentioning

confidence: 99%

Safety Improvement of Nuclear Power Reactor Using Soft Computing Techniques

Muzzammil¹

2013

IOSR-JECE

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“…To achieve the physical interpretability of the model, semantic constraints are imposed to the FSs obtained in the previous step in an attempt to obtain an "optimal" interface 26,27,28,29 . This is sought through the procedure described below in Sections 4.2.1-4.2.4; note that at each step of the procedure, the corresponding FSs modification required to achieve an improved physical interpretability is actually carried out only if the classification performance on the training data is not significantly decreased.…”

Section: Enforcement Of Appropriate Semantic Constraints On the Obtaimentioning

confidence: 99%

“…In order to avoid the overlapping among pairs of linguistic terms and the possible consequent semantic inconsistencies, it is necessary to have sufficiently distinct FSs 28 . If a FS X p j is too narrow, its contribution is too specific and model transparency is somewhat lost.…”

Section: Annihilation Of Narrow Fssmentioning

confidence: 99%

From Fuzzy Clustering to a Fuzzy Rule-Based Classification Model

Zio¹,

Baraldi²,

Popescu³

2008

IJCIS

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The applicability in practice of a diagnostic tool is strongly related to the physical transparency of the underlying models, for the interpretation of the relationships between the involved variables and for direct model inspection and validation. In this work, a methodology is developed for transforming an opaque, fuzzy clustering-based classification model into a fuzzy logic model based on transparent linguistic rules. These are obtained by cluster projection with appropriate coverage and distinguishability constraints onto the fuzzy input partitioning interface. The methodological approach is applied to a diagnostic task concerning the classification of simulated faults in the feedwater system of a nuclear Boiling Water Reactor.

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“…Artificial Neural Network (ANN), Support Vector Machine (SVM), Genetic Algorithm (GA) and Auto-Associative Kernel Regression (AAKR) are among some of the most studied and applied (Chevalier et al, 2009;Baraldi et al, 2010;Baradi et al, 2011;Santosh et al, 2009;Li et al, 2012;Yazikov et al, 2012;Rand et al, 2012a;Rand et al, 2012b;Muralidharan and Sugumaran, 2012;Ekici, 2012;Zio and Gola, 2006;Lu and Upadhyaya, 2005;Jeong et al, 2003;Zio et al, 2009). These approaches are already mature, especially for detection and diagnostics.…”

Section: Introductionmentioning

confidence: 99%

Nuclear power plant components condition monitoring by probabilistic support vector machine

Liu

Seraoui²,

Vitelli

et al. 2013

Annals of Nuclear Energy

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In this paper, an approach for the prediction of the condition of Nuclear Power Plant (NPP) components is proposed, for the purposes of condition monitoring. It builds on a modified version of the Probabilistic Support Vector Regression (PSVR) method, which is based on the Bayesian probabilistic paradigm with a Gaussian prior. Specific techniques are introduced for the tuning of the PSVR hyerparameters, the model identification and the uncertainty analysis. A real case study is considered, regarding the prediction of a drifting process parameter of a NPP component.

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Neuro-fuzzy pattern classification for fault diagnosis in nuclear components

Cited by 37 publications

References 9 publications

Safety Improvement of Nuclear Power Reactor Using Soft Computing Techniques

Safety Improvement of Nuclear Power Reactor Using Soft Computing Techniques

From Fuzzy Clustering to a Fuzzy Rule-Based Classification Model

Nuclear power plant components condition monitoring by probabilistic support vector machine

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